Selecting Counterions to Improve Ionized Hydrophilic Drug Encapsulation in Polymeric Nanoparticles.

Hydrophobic ion pairing (HIP) can successfully increase the drug loading and control the release kinetics of ionizable hydrophilic drugs, addressing challenges that prevent these molecules from reaching the clinic. Nevertheless, polymeric nanoparticle (PNP) formulation development requires trial-and-error experimentation to meet the target product profile, which is laborious and costly. Herein, we design a preformulation framework (solid-state screening, computational approach, and solubility in PNP-forming emulsion) to understand counterion-drug-polymer interactions and accelerate the PNP formulation development for HIP systems. The HIP interactions between a small hydrophilic molecule, AZD2811, and counterions with different molecular structures were investigated. Cyclic counterions formed amorphous ion pairs with AZD2811; the 0.7 pamoic acid/1.0 AZD2811 complex had the highest glass transition temperature (Tg; 162 °C) and the greatest drug loading (22%) and remained as phase-separated amorphous nanosized domains inside the polymer matrix. Palmitic acid (linear counterion) showed negligible interactions with AZD2811 (crystalline-free drug/counterion forms), leading to a significantly lower drug loading despite having similar log P and pKa with pamoic acid. Computational calculations illustrated that cyclic counterions interact more strongly with AZD2811 than linear counterions through dispersive interactions (offset π-π interactions). Solubility data indicated that the pamoic acid/AZD2811 complex has a lower organic phase solubility than AZD2811-free base; hence, it may be expected to precipitate more rapidly in the nanodroplets, thus increasing drug loading. Our work provides a generalizable preformulation framework, complementing traditional performance-indicating parameters, to identify optimal counterions rapidly and accelerate the development of hydrophilic drug PNP formulations while achieving high drug loading without laborious trial-and-error experimentation.

Predicting spinel solid solutions using a random atom substitution method.

Exploration of chemical composition and structural configuration space is the central problem in crystal structure prediction. Even in limiting structure space to a single structure type, many different compositions and configurations are possible. In this work, we attempt to address this problem using an extension to the existing ChemDASH code in which variable compositions can be explored. We show that ChemDASH is an efficient method for exploring a fixed-composition space of spinel structures and build upon this to include variable compositions in the Mn-Fe-Zn-O spinel phase field. This work presents the first basin-hopping crystal structure prediction method that can explore variable compositions.

Local practices and production confer resilience to rural Pacific food systems during the COVID-19 pandemic.

Resilience of food systems is key to ensuring food security through crisis. The COVID-19 pandemic presents an unprecedented shock that reveals varying levels of resilience of increasingly interconnected food systems across the globe. We contribute to the ongoing debate about whether increased connectivity reduces or enhances resilience in the context of rural Pacific food systems, while examining how communities have adapted to the global shocks associated with the pandemic to ensure food security. We conducted 609 interviews across 199 coastal villages from May to October 2020 in Federated States of Micronesia, Fiji, Palau, Papua New Guinea, Solomon Islands, Tonga, and Tuvalu to understand community-level impacts and adaptations during the first 5-10 months of the COVID-19 crisis. We found that local food production practices and food sharing conferred resilience, and that imported foods could aid or inhibit resilience. Communities in countries more reliant on imports were almost twice as likely to report food insecurity compared to those least reliant. However, in places dealing with a concurrent cyclone, local food systems were impaired, and imported foods proved critical. Our findings suggest that policy in the Pacific should bolster sustainable local food production and practices. Pacific states should avoid becoming overly reliant on food imports, while having measures in place to support food security after disasters, supplementing locally produced and preserved foods with imported foods when necessary. Developing policies that promote resilient food systems can help prepare communities for future shocks, including those anticipated with climate change.

Simulation of protein pulling dynamics on second time scale with boxed molecular dynamics.

We demonstrate how recently developed Boxed Molecular Dynamics (BXD) and kinetics [D. V. Shalashilin et al., J. Chem. Phys. 137, 165102 (2012)] can provide a kinetic description of protein pulling experiments, allowing for a connection to be made between experiment and the atomistic protein structure. BXD theory applied to atomic force microscopy unfolding is similar in spirit to the kinetic two-state model [A. Noy and R. W. Friddle, Methods 60, 142 (2013)] but with some differences. First, BXD uses a large number of boxes, and therefore, it is not a two-state model. Second, BXD rate coefficients are obtained from atomistic molecular dynamics simulations. BXD can describe the dependence of the pulling force on pulling speed. Similar to Shalashilin et al. [J. Chem. Phys. 137, 165102 (2012)], we show that BXD is able to model the experiment at a very long time scale up to seconds, which is way out of reach for standard molecular dynamics.

Mechanical Unfolding of Proteins-A Comparative Nonequilibrium Molecular Dynamics Study.

Mechanical signals regulate functions of mechanosensitive proteins by inducing structural changes that are determinant for force-dependent interactions. Talin is a focal adhesion protein that is known to extend under mechanical load, and it has been shown to unfold via intermediate states. Here, we compared different nonequilibrium molecular dynamics (MD) simulations to study unfolding of the talin rod. We combined boxed MD (BXD), steered MD, and umbrella sampling (US) techniques and provide free energy profiles for unfolding of talin rod subdomains. We conducted BXD, steered MD, and US simulations at different detail levels and demonstrate how these different techniques can be used to study protein unfolding under tension. Unfolding free energy profiles determined by BXD suggest that the intermediate states in talin rod subdomains are stabilized by force during unfolding, and US confirmed these results.

NMR and Thermal Studies for the Characterization of Mass Transport and Phase Separation in Paracetamol/Copovidone Hot-Melt Extrusion Formulations.

The formulation of drug/polymer amorphous solid dispersions (ASDs) is one of the most successful strategies for improving the oral bioavailability of poorly soluble active pharmaceutical ingredients (APIs). Hot-melt extrusion (HME) is one method for preparing ASDs that is growing in importance in the pharmaceutical industry, but there are still substantial gaps in our understanding regarding the dynamics of drug dissolution and dispersion in viscous polymers and the physical stability of the final formulations. Furthermore, computational models have been built to predict optimal processing conditions, but they are limited by the lack of experimental data for key mass transport parameters, such as the diffusion coefficient. The work presented here reports direct measurements of API diffusion in pharmaceutical polymer melts, using high-temperature pulsed-field gradient NMR. The diffusion coefficient of a model drug/polymer system (paracetamol/copovidone) was determined for different drug loadings and at temperatures relevant to the HME process. The mechanisms of the diffusion process are also explored with the Stokes-Einstein and Arrhenius models. The results show that diffusivity is linked exponentially to temperature. Furthermore, this study includes rheological characterization, differential scanning calorimetry (DSC), and 1H ssNMR T1 and T1ρ measurements to give additional insights into the physical state, phase separation, and API/polymer interactions in paracetamol/copovidone ASD formulations.

Prognostic value of multiparametric magnetic resonance imaging, transient elastography and blood-based fibrosis markers in patients with chronic liver disease.

BACKGROUND & AIMS: Liver cT1 , liver T1 , transient elastography (TE) and blood-based biomarkers have independently been shown to predict clinical outcomes but have not been directly compared in a single cohort of patients. Our aim was to compare these tests' prognostic value in a cohort of patients with compensated chronic liver disease. METHODS: Patients with unselected compensated liver disease aetiologies had baseline assessments and were followed up for development of clinical outcomes, blinded to the imaging results. The prognostic value of non-invasive liver tests at prespecified thresholds was assessed for a combined clinical endpoint comprising ascites, variceal bleeding, hepatic encephalopathy, hepatocellular carcinoma, liver transplantation and mortality. RESULTS: One hundred and ninety-seven patients (61% male) with median age of 54 years were followed up for 693 patient-years (median (IQR) 43 (26-58) months). The main diagnoses were NAFLD (41%), viral hepatitis (VH, 25%) and alcohol-related liver disease (ArLD; 14%). During follow-up 14 new clinical events, and 11 deaths occurred. Clinical outcomes were predicted by liver cT1  > 825ms with HR 9.9 (95% CI: 1.29-76.4, P = .007), TE > 8kPa with HR 7.8 (95% CI: 0.97-62.3, P = .02) and FIB-4 > 1.45 with HR 4.09 (95% CI: 0.90-18.4, P = .05). In analysis taking into account technical failure and unreliability, liver cT1  > 825 ms could predict clinical outcomes (P = .03), but TE > 8kPa could not (P = .4). CONCLUSIONS: We provide further evidence that liver cT1 , TE and serum-based biomarkers can predict clinical outcomes, but when taking into account technical failure/unreliability, TE cut-offs perform worse than those of cT1 and blood biomarkers.

Inexpensive Methods for Live Imaging of Central Pattern Generator Activity in the Drosophila Larval Locomotor System.

Central pattern generators (CPGs) are neural networks that produce rhythmic motor activity in the absence of sensory input. CPGs produce 'fictive' behaviours in vitro which parallel activity seen in intact animals. CPG networks have been identified in a wide variety of model organisms and have been shown to be critical for generating rhythmic behaviours such as swimming, walking, chewing and breathing. Work with CPG preparations has led to fundamental advances in neuroscience; however, most CPG preparations involve intensive dissections and require sophisticated electrophysiology equipment, making export to teaching laboratories problematic. Here we present an integrated approach for bringing the study of locomotor CPGs in Drosophila larvae into teaching laboratories. First, we present freely available genetic constructs that enable educators to express genetically encoded calcium indicators in cells of interest in the larval central nervous system. Next, we describe how to isolate the larval central nervous system and prepare it for live imaging. We then show how to modify standard compound microscopes to enable fluorescent imaging using 3D printed materials and inexpensive optical components. Finally, we show how to use the free image analysis programme ImageJ and freely available features in the signal analysis programme DataView to analyse rhythmic CPG activity in the larval CNS. Comparison of results to those obtained on research equipment shows that signal-to-noise levels are comparable and core features of larval CPG activity can be observed. Overall, this work shows the viability of exporting live imaging experiments to low cost environments and paves the way for new teaching laboratory exercises revolving around optical imaging of CPG activity.

In Silico Screening for Solid Dispersions: The Trouble with Solubility Parameters and χFH.

The problem of predicting small molecule-polymer compatibility is relevant to many areas of chemistry and pharmaceutical science but particularly drug delivery. Computational methods based on Hildebrand and Hansen solubility parameters, and the estimation of the Flory-Huggins parameter, χ, have proliferated across the literature. Focusing on the need to develop amorphous solid dispersions to improve the bioavailability of poorly soluble drug candidates, an innovative, high-throughput 2D printing method has been employed to rapidly assess the compatibility of 54 drug-polymer pairings (nine drug compounds in six polymers). In this study, the first systematic assessment of the in silico methods for this application, neither the solubility parameter approach nor the calculated χ, correctly predicted drug-polymer compatibility. The theoretical limitations of the solubility parameter approach are discussed and used to explain why this approach is fundamentally unsuitable for predicting polymer-drug interactions. Examination of the original sources describing the method for calculating χ shows that only the enthalpic contributions to the term have been included, and the corrective entropic term is absent. The development and application of new in silico techniques, that consider all parts of the free energy of mixing, are needed in order to usefully predict small molecule-polymer compatibility and to realize the ambition of a drug-polymer screening method.

Using neutrons, X-rays and nuclear magnetism to determine the role of transition metal oxide inclusions on both glass structure and stability in automotive glass enamels.

Low levels of transition metal oxides in alkali borosilicate glass systems can drastically influence crystallisation and phase separation properties. We investigated the non-monotonic effect of manganese doping on suppressing crystallisation, and the influence on optical properties by iron oxide doping, in terms of local atomic structure. Structural models based on empirical potential structure refinement were generated from neutron and X-ray scattering data, and compared against multinuclear solid-state NMR. This revealed that a 2.5% manganese doping had a disruptive effect on the entire glass network, supressing crystallisation of an undesired bismuth silicate phase, and that iron species preferentially locate near borate tetrahedra. Preventing phase separation and controlling crystallisation behaviour of glass are critical to the ultimate properties of automotive glass enamels.

Rapid Nanogram Scale Screening Method of Microarrays to Evaluate Drug-Polymer Blends Using High-Throughput Printing Technology.

A miniaturized, high-throughput assay was optimized to screen polymer-drug solid dispersions using a 2-D Inkjet printer. By simply printing nanoliter amounts of polymer and drug solutions onto an inert surface, drug/polymer microdots of tunable composition were produced in an easily addressable microarray format. The amount of material printed for each dried spot ranged from 25 ng to 650 ng. These arrays were used to assess the stability of drug/polymer dispersions with respect to recrystallization, using polarized light microscopy. One array with a panel of 6 drugs formulated at different ratios with a poly(vinylpyrrolidone-vinyl acetate) (PVPVA) copolymer was developed to estimate a possible bulk (gram-scale) approximation threshold from the final printed nanoamount of formulation. Another array was printed at a fixed final amount of material to establish a literature comparison of one drug formulated with different commercial polymers for validation. This new approach may offer significant efficiency in pharmaceutical formulation screening, with each experiment in the nanomicro-array format requiring from 3 up to 6 orders of magnitude lower amounts of sample than conventional screening methods.

Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease.

BACKGROUND & AIMS: Multiparametric magnetic resonance (MR) imaging has been demonstrated to quantify hepatic fibrosis, iron, and steatosis. The aim of this study was to determine if MR can be used to predict negative clinical outcomes in liver disease patients. METHODS: Patients with chronic liver disease (n=112) were recruited for MR imaging and data on the development of liver related clinical events were collected by medical records review. The median follow-up was 27months. MR data were analysed blinded for the Liver Inflammation and Fibrosis score (LIF; <1, 1-1.99, 2-2.99, and ⩾3 representing normal, mild, moderate, and severe liver disease, respectively), T2∗ for liver iron content and proportion of liver fat. Baseline liver biopsy was performed in 102 patients. RESULTS: Liver disease aetiologies included non-alcoholic fatty liver disease (35%) and chronic viral hepatitis (30%). Histologically, fibrosis was mild in 54 (48%), moderate in 17 (15%), and severe in 31 (28%) patients. Overall mortality was 5%. Ten patients (11%) developed at least one liver related clinical event. The negative predictive value of LIF<2 was 100%. Two patients with LIF 2-2.99 and eight with LIF⩾3 had a clinical event. Patients with LIF⩾3 had a higher cumulative risk for developing clinical events, compared to those with LIF<1 (p=0.02) and LIF 1-1.99 (p=0.03). Cox regression analysis including all 3 variables (fat, iron, LIF) resulted in an enhanced LIF predictive value. CONCLUSIONS: Non-invasive standardised multiparametric MR technology may be used to predict clinical outcomes in patients with chronic liver disease.

Indomethacin-Kollidon VA64 Extrudates: A Mechanistic Study of pH-Dependent Controlled Release.

Because of its weakly acidic nature (pKa of 4.5), indomethacin presents an aqueous solubility that significantly increases when changing from acidic to neutral/alkaline pH (1.5 µg/mL at pH 1.2 and 105.2 µg/mL at pH 7.4). We have therefore investigated the impact of the dissolution medium pH on the dissolution performance of indomethacin:Kollidon VA64 extrudates. The impact of the drug loading on the dissolution properties of these systems was also examined (5%, 15%, 30%, 50%, 70%, and 90% drug loading). Time-resolved Raman spectroscopy along with in-line UV-vis spectrophotometry was employed to directly relate changes in dissolution behavior to physicochemical changes that occur to the extrudate during the test. The dissolution tests were performed in pH 2 HCl (to mimic the stomach conditions), and this was then switched during the experiment to pH 6.8 phosphate buffer (to simulate the poststomach conditions). The rotating disc dissolution rate test was also used to simultaneously measure the dissolution rate of both the drug and the polymer. We found that in pH 2 HCl buffer, for the 15% or higher drug-loaded extrudates, Kollidon VA64 preferentially dissolves from the exterior of the compact leaving an amorphous drug-rich hydrophobic shell, which, similarly to an enteric coating, inhibits the drug release. The in situ formation of an enteric coating has been previously hypothesized, and this has been the first time that is directly observed in a pH-variable dissolution test. The dissolution medium switch to pH 6.8 phosphate buffer, due to the large increase of the aqueous solubility of indomethacin at this pH, leads to rapid dissolution of the material forming the coating and therefore total drug release. In contrast, the 5% extrudate is fully hydrated and quickly dissolves at low pH pointing to a dissolution performance dependent on highly water-soluble Kollidon VA64.

Long-Term Amorphous Drug Stability Predictions Using Easily Calculated, Predicted, and Measured Parameters.

The purpose of this study was to develop a predictive model of the amorphous stability of drugs with particular relevance for poorly water-soluble compounds. Twenty-five representative neutral poorly soluble compounds with a diverse range of physicochemical properties and chemical structures were systematically selected from an extensive library of marketed drug products. The physical stability of the amorphous form, measured over a 6 month period by the onset of crystallization of amorphous films prepared by melting and quench-cooling, was assessed using polarized light microscopy. The data were used as a response variable in a statistical model with calculated/predicted or measured molecular, thermodynamic, and kinetic parameters as explanatory variables. Several multiple linear regression models were derived, with varying balance between calculated/predicted and measured parameters. It was shown that inclusion of measured parameters significantly improves the predictive ability of the model. The best model demonstrated a prediction accuracy of 82% and included the following as parameters: melting and glass transition temperatures, enthalpy of fusion, configurational free energy, relaxation time, number of hydrogen bond donors, lipophilicity, and the ratio of carbon to heteroatoms. Good predictions were also obtained with a simpler model, which was comprised of easily acquired quantities: molecular weight and enthalpy of fusion. Statistical models are proposed to predict long-term amorphous drug stability. The models include readily accessible parameters, which are potentially the key factors influencing amorphous stability. The derived models can support faster decision making in drug formulation development.

Monitoring the Dissolution Mechanisms of Amorphous Bicalutamide Solid Dispersions via Real-Time Raman Mapping.

Real-time in situ Raman mapping has been employed to monitor, during dissolution, the crystallization transitions of amorphous bicalutamide formulated as a molecular dispersion in a copovidone VA64 matrix. The dissolution performance was also investigated using the rotating disc dissolution rate methodology, which allows simultaneous determination of the dissolution rate of both active ingredient and polymer. The dissolution behavior of two bicalutamide:copovidone VA64 dispersion formulations, containing 5% (w/w) and 50% (w/w) bicalutamide, respectively, was investigated, with the aim of exploring the effect of increasing the bicalutamide loading on the dissolution performance. Spatially time-resolved Raman maps generated using multivariate curve resolution indicated the simultaneous transformation of amorphous bicalutamide present in the 50% drug-loaded extrudate into metastable polymorphic form II and low-energy polymorphic form I. Fitting a kinetic model and spatially correlating the data extracted from the Raman maps also allowed us to understand the re-crystallization mechanisms by which the low-energy form I appears. Form I was shown to crystallize mainly directly from the amorphous solid dispersion, with crystallization from the metastable form II being a minor contribution.

Hydrotrope accumulation around the drug: the driving force for solubilization and minimum hydrotrope concentration for nicotinamide and urea.

Nicotinamide is an effective non-micellar hydrotrope (solubilizer) for drugs with low aqueous solubility. To clarify the molecular basis of nicotinamide's hydrotropic effectiveness, we present here a rigorous statistical thermodynamic theory, based on the Kirkwood-Buff theory of solutions, and our recent application of it to hydrotropy. We have shown that (i) nicotinamide self-association reduces solubilization efficiency, contrary to the previous hypothesis which claimed that self-association drives solubilization and (ii) the minimum hydrotrope concentration (MHC), namely, the threshold concentration above which solubility suddenly increases, is caused not by the bulk-phase self-association of nicotinamides as has been postulated previously, but by the enhancement of nicotinamide-nicotinamide interaction around the drug molecules. We have thus established a new view of hydrotropy - it is nicotinamide's non-stoichiometric accumulation around the drug that is the basis of solubility increase above MHC.

Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease.

BACKGROUND & AIMS: With the increasing prevalence of liver disease worldwide, there is an urgent clinical need for reliable methods to diagnose and stage liver pathology. Liver biopsy, the current gold standard, is invasive and limited by sampling and observer dependent variability. In this study, we aimed to assess the diagnostic accuracy of a novel magnetic resonance protocol for liver tissue characterisation. METHODS: We conducted a prospective study comparing our magnetic resonance technique against liver biopsy. The individual components of the scanning protocol were T1 mapping, proton spectroscopy and T2* mapping, which quantified liver fibrosis, steatosis and haemosiderosis, respectively. Unselected adult patients referred for liver biopsy as part of their routine care were recruited. Scans performed prior to liver biopsy were analysed by physicians blinded to the histology results. The associations between magnetic resonance and histology variables were assessed. Receiver-operating characteristic analyses were also carried out. RESULTS: Paired magnetic resonance and biopsy data were obtained in 79 patients. Magnetic resonance measures correlated strongly with histology (r(s)=0.68 p<0.0001 for fibrosis; r(s)=0.89 p<0.001 for steatosis; r(s)=-0.69 p<0.0001 for haemosiderosis). The area under the receiver operating characteristic curve was 0.94, 0.93, and 0.94 for the diagnosis of any degree of fibrosis, steatosis and haemosiderosis respectively. CONCLUSION: The novel scanning method described here provides high diagnostic accuracy for the assessment of liver fibrosis, steatosis and haemosiderosis and could potentially replace liver biopsy for many indications. This is the first demonstration of a non-invasive test to differentiate early stages of fibrosis from normal liver.

Wireless magnetoelectrically powered organic light-emitting diodes.

Compact wireless light sources are a fundamental building block for applications ranging from wireless displays to optical implants. However, their realization remains challenging because of constraints in miniaturization and the integration of power harvesting and light-emission technologies. Here, we introduce a strategy for a compact wirelessly powered light-source that consists of a magnetoelectric transducer serving as power source and substrate and an antiparallel pair of custom-designed organic light-emitting diodes. The devices operate at low-frequency ac magnetic fields (~100 kHz), which has the added benefit of allowing operation multiple centimeters deep inside watery environments. By tuning the device resonance frequency, it is possible to separately address multiple devices, e.g., to produce light of distinct colors, to address individual display pixels, or for clustered operation. By simultaneously offering small size, individual addressing, and compatibility with challenging environments, our devices pave the way for a multitude of applications in wireless displays, deep tissue treatment, sensing, and imaging.

Using a systematic and quantitative approach to generate new insights into drug loading of PLGA nanoparticles using nanoprecipitation.

The synthesis of drug-loaded PLGA nanoparticles through nanoprecipitation in solvent/antisolvent mixtures is well reported but lacks clarity in explaining drug loading mechanisms and the prediction of efficiency of drug entrapment. Various methods using physical parameters such as log P and solid-state drug-polymer solubility aim to predict the intensity of drug-polymer interactions but lack precision. In particular, the zero-enthalpy method for drug/polymer solubility may be intrinsically inaccurate, as we demonstrate. Conventional measurement of loading capacity (LC), expressed in weight ratios, can be misleading for comparing different drugs and we stress the importance of using molar units. This research aims to provide new insights and critically evaluate the established methodologies for drug loading of PLGA nanoparticles. The study employs four model drugs with varying solubilities in solvent/antisolvent mixtures, log P values, and solid-state solubility in PLGA: ketoprofen (KPN), indomethacin (IND), sorafenib (SFN), and clofazimine (CFZ). This study highlights that drug loading efficiency is primarily influenced by the drug's solubilities within the solvent system. We emphasise that both kinetic and thermodynamic factors play a role in the behaviour of the system by considering the changes in drug solubility during mixing. The study introduces a pseudo-constant K* to characterise drug-polymer interactions, with CFZ and SFN showing the highest K* values. Interestingly, while IND and KPN have lower K* values, they achieve higher loading capacities due to their greater solubilities, indicating the key role of solubility in determining LC.